Yearly Traffic Safety Analysis

2,159 CRASHES IN
IOWA, IA
2017

All metrics benchmarked against2016

In 2017, Black Hawk County recorded 2,159 total crashes, a 5.4% decrease from the 2,283 crashes reported in 2016. While overall crashes, fatalities, and injuries declined, the number of crashes involving an impaired driver increased from 66 in 2016 to 80 in 2017, a rise of 21.2%.

2,159

▼ -5.4%was 2,283

Total Crash Events

8

▼ -27.3%was 11

Persons Killed

787

▼ -5.0%was 828

Persons Injured

8

▼ -27.3%was 11

Fatal Crash Events

Note: "Persons Killed" (8) counts individual fatalities across all crash events. "Fatal" in the severity table below (8) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Traffic collisions in Black Hawk County showed a downward trend from 2016 to 2017. Total crashes decreased by 5.4%, from 2,283 to 2,159. This decline was also reflected in crash outcomes, with total fatalities dropping from 11 to 8 and total injuries decreasing from 828 to 787.

Vulnerable Road User Casualties

2

Pedestrians Killed

Prior: 0→%

0

Cyclists Killed

Prior: 0→0.0%

6

Motorists Killed

Prior: 11↓-45.5%

17

Pedestrians Injured

Prior: 19↓-10.5%

20

Cyclists Injured

Prior: 24↓-16.7%

750

Motorists Injured

Prior: 784↓-4.3%

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The temporal patterns of crashes in Black Hawk County remained consistent year-over-year. Friday was the peak day for crashes in both 2017 (397 crashes) and 2016 (393 crashes). Similarly, the 3 p.m. hour remained the peak time for collisions in both periods, although the number of crashes during that hour decreased from 207 in 2016 to 184 in 2017.

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

The severity of crashes saw a slight improvement from 2016 to 2017. The number of fatal crashes decreased from 11 to 8, and their share of all crashes fell from 0.5% to 0.4%. Crashes resulting in serious injuries also saw a notable drop in count, decreasing from 46 in 2016 to 29 in 2017. The overall proportion of crashes involving any level of injury remained stable at approximately 30% for both years.

Outcome by Severity (Crash Events)

Fatal8fatal crashes0.4%
↓-27.3%prior 11
Serious Injury29serious injury crashes1.3%
↓-37.0%prior 46
Minor Injury201minor injury crashes9.3%
↓-8.2%prior 219
Possible Injury409possible injury crashes18.9%
↓-1.7%prior 416
No Injury1,512no injury crashes70%
↓-5.0%prior 1,591

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Most severe injury per crash record

Top Contributing Factors

The leading contributing factors for crashes showed some shifts in rank and count between 2016 and 2017. 'Followed too close' and 'Other' remained the top two causes, with counts of 204 and 208 respectively in 2017, very similar to the prior year. Crashes involving an 'Animal' decreased by 6.4% in count, from 187 to 175. Notably, crashes attributed to 'Driving too fast for conditions' saw a significant 33.9% drop in count, from 124 incidents in 2016 to 82 in 2017.

Officer-Reported Primary Contributing Cause

Other (explain in narrative): Other208 (9.6%)↑6.1%prior 196
Followed too close204 (9.4%)↑0.5%prior 203
Animal175 (8.1%)↓-6.4%prior 187
FTYROW: From stop sign142 (6.6%)↓-9.6%prior 157
Ran Traffic Signal129 (6%)→0.0%prior 129
FTYROW: Making left turn117 (5.4%)↓-19.9%prior 146
Ran off road - left111 (5.1%)↑5.7%prior 105
Lost Control94 (4.4%)↑6.8%prior 88
Ran Stop Sign89 (4.1%)↓-4.3%prior 93
Driving too fast for conditions82 (3.8%)↓-33.9%prior 124

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The environmental conditions at the time of crashes showed some year-over-year changes. While the proportion of crashes in daylight and clear weather remained largely stable, there was a notable decrease in collisions occurring on adverse road surfaces. In 2017, 20.5% of crashes happened on wet, snowy, or icy roads, down from 26.2% in 2016. This was driven by a reduction in crashes on snow-covered roads (from 173 to 113) and icy roads (from 121 to 57).

Weather

Clear1,354 (66.3%)
↓-2.7%prior 1,391
Cloudy404 (19.8%)
↓-4.9%prior 425
Rain129 (6.3%)
↓-0.8%prior 130
Snow90 (4.4%)
↓-19.6%prior 112
Freezing rain/drizzle30 (1.5%)
↑25.0%prior 24
Blowing Snow18 (0.9%)
↓-21.7%prior 23
Fog, smoke, smog15 (0.7%)
↓-40.0%prior 25
Other (explain in narrative)2 (0.1%)
Severe Winds1 (0.0%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Weather condition at time of crash

Lighting

Daylight1,411 (68.8%)
↓-6.6%prior 1,510
Dark - roadway lighted384 (18.7%)
↑1.3%prior 379
Dark - roadway not lighted176 (8.6%)
↑10.7%prior 159
Dusk38 (1.9%)
↓-7.3%prior 41
Dawn32 (1.6%)
↓-8.6%prior 35
Dark - unknown roadway lighting11 (0.5%)
↓-38.9%prior 18

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Lighting condition field

Road Surface

Dry1,583 (77.4%)
↑3.3%prior 1,533
Wet263 (12.9%)
↓-2.6%prior 270
Snow113 (5.5%)
↓-34.7%prior 173
Ice/frost57 (2.8%)
↓-52.9%prior 121
Gravel12 (0.6%)
↑33.3%prior 9
Slush9 (0.4%)
↓-73.5%prior 34
Other (explain in narrative)4 (0.2%)
Mud, dirt3 (0.1%)
Sand2 (0.1%)

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Road surface condition field

Vehicles & Demographics

The composition of vehicles and persons involved in crashes remained largely consistent between 2016 and 2017. Ford and Chevrolet were the most common vehicle makes involved in collisions in both years, with their relative positions at the top of the list unchanged. An analysis of persons involved shows a decrease in numbers across most age groups, mirroring the overall decline in total crashes, with no significant shifts in the proportional representation of any single age demographic.

Top Vehicle Makes (3,930 vehicles)

1
FORD595 (15.1%)
↓-2.9%prior 613
2
CHEV573 (14.6%)
↑18.4%prior 484
3
CHEVROLET249 (6.3%)
↓-39.0%prior 408
4
TOYT215 (5.5%)
↑36.1%prior 158
5
DODG167 (4.2%)
↑14.4%prior 146
6
JEEP123 (3.1%)
↑17.1%prior 105
7
PONT121 (3.1%)
↑22.2%prior 99
8
CHRY109 (2.8%)
↑16.0%prior 94
9
GMC105 (2.7%)
↓-10.3%prior 117
10
NR101 (2.6%)
↑7.4%prior 94

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Vehicle unit records

621 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (3,099 persons with recorded sex)

Male1,674 (54.0%)
↓-3.3%prior 1,732
Female1,425 (46.0%)
↓-8.7%prior 1,560

Source: Iowa Crash Data · ArcGIS Open Data · 2017-01-01 to 2017-12-31 · Person-level records linked to crash events

Data Sources & Methodology

Primary Data Source

All crash data in this report is sourced from Iowa Crash Data, accessed programmatically via the ArcGIS Open Data API (SODA). This dataset contains official police-reported motor vehicle traffic crash records maintained by the reporting jurisdiction's law enforcement agency. Records are published to the open data portal by the municipality and are subject to the portal's terms of use.

Data Retrieval

  • Access method: ArcGIS Open Data API (SoQL queries)
  • Data format: Structured JSON via REST API
  • Record types queried: Crash events, person records, and vehicle unit records
  • Date filter applied: 2017-01-01 through 2017-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2017-01-01 through 2017-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 2,159
  • Total persons involved: 4,577
  • Total vehicles involved: 3,930

Analytical Methodology

  • Severity classification: Uses the KABCO injury scale (K=Fatal, A=Incapacitating injury, B=Non-incapacitating injury, C=Possible injury, O=No injury/property damage only), the standard classification in U.S. Model Minimum Uniform Crash Criteria (MMUCC). Severity is assigned per crash event based on the most severe injury in that crash. A single fatal crash (K) may involve multiple fatalities; therefore the "Persons Killed" count in the headline KPIs may differ from the "Fatal" crash count in the severity breakdown.
  • Contributing factors: Reflect the officer-determined primary contributory cause recorded at the time of the crash report. These are preliminary determinations and may not reflect final investigation findings.
  • Hit-and-run classification: Based on the hit-and-run indicator field in the official crash report, as determined by the responding officer at the scene.
  • Temporal analysis: Day-of-week and hour-of-day distributions are computed from the crash date/time timestamp in each record.
  • Demographics: Age and sex distributions are drawn from person-level records linked to each crash event. A single crash may involve multiple persons.
  • Vehicle data: Make information is drawn from vehicle unit records linked to each crash event.
  • AI commentary: Narrative sections are generated by Google Gemini (large language model) based on the structured data. Commentary is descriptive, not predictive, and should not be interpreted as expert opinion.

Limitations & Disclaimers

  • Only crashes reported to and documented by law enforcement are included. Minor incidents, unreported crashes, and near-misses are not captured in this dataset.
  • Data reflects conditions at the time of the initial police report and may be subject to subsequent corrections, reclassifications, or supplements by the reporting agency.
  • Open data portal records may experience a publication lag - recently occurring crashes may not yet appear in the dataset at the time of report generation.
  • AI-generated commentary is produced by a large language model and is intended to highlight patterns in the data. It does not constitute legal, medical, or professional analysis.
  • Percentages are calculated from reported data and are subject to rounding.

Non-Affiliation Disclosure

This report is produced independently by ThatCarHitMe.com (Injuria.ai). It is not affiliated with, endorsed by, or produced in partnership with any law enforcement agency, municipal government, state department of transportation, or the National Highway Traffic Safety Administration (NHTSA). Data is sourced from publicly available government open data portals.

Data License

The underlying crash data is provided under the municipality's Open Data Terms of Use and is made available to the public for unrestricted use. This analysis and report is © 2026 Injuria.ai and may be cited with attribution using the suggested citation below.

Corrections & Feedback

If you believe any data in this report is inaccurate or have questions about our methodology, please contact: data@injuria.ai. We are committed to accuracy and will issue corrections promptly.

Suggested Citation

ThatCarHitMe.com (Injuria.ai). "iowa, IA Crash Intelligence Report: 2017." Published September 9, 2026. Reporting period: 2017-01-01 to 2017-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2017-annual-report

About the Publisher

ThatCarHitMe.com is a crash data intelligence platform developed by Injuria.ai, a legal technology company specializing in traffic safety analytics. We aggregate and analyze publicly available government crash data to produce structured intelligence reports for communities, researchers, journalists, and legal professionals. Our reports combine programmatic data retrieval from official open data portals with AI-assisted narrative analysis.

Questions about this report's data or methodology: data@injuria.ai

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